[New!] I recently joined Anthropic
as a member of technical staff.
Prior to that, I completed my
PhD in computer science at
Carnegie Mellon University, where I
studied gradient-based optimization, data-centric AI, distributed ML
systems, and ML tooling, under the guidance of
Eric Xing (PhD) and
Jaime Carbonell (MS). Before that,
I earned BS in Electrical Computer Engineering & Mathematics (double major) from
Seoul National University.
I had also spent time as a research intern at
Microsoft in 2021.
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
Preprint, 2024
Sang Keun Choe, Hwijeen Ahn*, Juhan Bae*, Kewen Zhao*, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Grosse, and Eric Xing
Making Scalable Meta Learning Practical
NeurIPS, 2023
Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, and Eric Xing
Betty: An Automatic Differentiation Library for Multilevel Optimization
[code]
ICLR, 2023
Sang Keun Choe, Willie Neiswanger, Pengtao Xie, and Eric Xing
Oral (1.8% acceptance rate)
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
[code]
OSDI, 2021
Aurick Qiao, Sang Keun Choe, Suhas Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Greg Ganger, Eric Xing
🏆 Jay Lepreau Best Paper Award
On Orthogonal Jacobian Regularization in Deep Neural Networks
SEDL Workshop @ NeurIPS, 2019
Sang Keun Choe*, Hosan Jeong*, Jaime Carbonell
On Leveraging the Visual Modality for Neural Machine Translation
INLG, 2019
Vikas Raunak*, Sang Keun Choe*, Quanyang Lu*, Yi Xu*, Florian Metze
Audio Cover Song Identification using Convolutional Neural Network ICASSP, 2017
Sungkyung Chang, Juheon Lee, Sang Keun Choe, Kyogu Lee